Object recognition is a computer vision task with applications in disciplines such as security, optical character/digit/document recognition, industrial inspection, content-based image retrieval (CIBR), robotics, medical imaging, intelligent navigation systems, augmented reality, among others. In the field of security, for example, object recognition can be used for keyless access for buildings or computing devices via facial or biometric recognition (e.g., iris or fingerprint recognition) or video surveillance for identifying potential intruders. In the field of augmented reality, object recognition can be utilized to enhance interaction with physical objects. For instance, a live view of the physical object can be captured and displayed on a computing device, and metadata relating to the physical object can be overlayed upon the live view of the physical object after the physical object has been identified. Accordingly, a user interested in acquiring information about a book or DVD in his or her proximity, can capture an image of the book or DVD and submit the captured image to an object recognition system to obtain information associated with the book or DVD. To accomplish this, local features of the captured image can be extracted and compared to feature descriptors stored in a database of the object recognition system. Once a match is identified, information associated with the matching image (e.g., synopsis, availability, or other information for purchasing the book or DVD) can be provided and displayed to the user on his or her computing device. Not all items, however, may be as feature-rich or texture-rich as books or DVDS. For example, items such as computing devices (e.g., laptop computers, smart phones, tablets, e-book readers), displays (e.g., televisions, monitors, all-in-one desktop computers), or kitchen appliances (e.g., stoves, refrigerators, microwaves) may be feature-sparse or have limited or no texture. That is, the surfaces of these items may be generally uniform and/or lacking in interest points such that these items that may not be as amenable to conventional object recognition approaches, and identifying such items may be less successful.